GSplit LBI: Taming the Procedural Bias in Neuroimaging for Disease Prediction

In voxel-based neuroimage analysis, lesion features have been the main focus in disease prediction due to their interpretability with respect to the related diseases. However, we observe that there exists another type of features introduced during the preprocessing steps and we call them "\textbf{Procedural Bias}". Besides, such bias can be leveraged to improve classification accuracy. Nevertheless, most existing models suffer from either under-fit without considering procedural bias or poor interpretability without differentiating such bias from lesion ones. In this paper, a novel dual-task algorithm namely \emph{GSplit LBI} is proposed to resolve this problem. By introducing an augmented variable enforced to be structural sparsity with a variable splitting term, the estimators for prediction and selecting lesion features can be optimized separately and mutually monitored by each other following an iterative scheme. Empirical experiments have been evaluated on the Alzheimer's Disease Neuroimaging Initiative\thinspace(ADNI) database. The advantage of proposed model is verified by improved stability of selected lesion features and better classification results.Comment: Conditional Accepted by Miccai,201

CompNet: Complementary Segmentation Network for Brain MRI Extraction

Brain extraction is a fundamental step for most brain imaging studies. In this paper, we investigate the problem of skull stripping and propose complementary segmentation networks (CompNets) to accurately extract the brain from T1-weighted MRI scans, for both normal and pathological brain images. The proposed networks are designed in the framework of encoder-decoder networks and have two pathways to learn features from both the brain tissue and its complementary part located outside of the brain. The complementary pathway extracts the features in the non-brain region and leads to a robust solution to brain extraction from MRIs with pathologies, which do not exist in our training dataset. We demonstrate the effectiveness of our networks by evaluating them on the OASIS dataset, resulting in the state of the art performance under the two-fold cross-validation setting. Moreover, the robustness of our networks is verified by testing on images with introduced pathologies and by showing its invariance to unseen brain pathologies. In addition, our complementary network design is general and can be extended to address other image segmentation problems with better generalization.Comment: 8 pages, Accepted to MICCAI 201

Molecular basis for passive immunotherapy of Alzheimer's disease

Amyloid aggregates of the amyloid-{beta} (A{beta}) peptide are implicated in the pathology of Alzheimer's disease. Anti-A{beta} monoclonal antibodies (mAbs) have been shown to reduce amyloid plaques in vitro and in animal studies. Consequently, passive immunization is being considered for treating Alzheimer's, and anti-A{beta} mAbs are now in phase II trials. We report the isolation of two mAbs (PFA1 and PFA2) that recognize A{beta} monomers, protofibrils, and fibrils and the structures of their antigen binding fragments (Fabs) in complex with the A{beta}(1–8) peptide DAEFRHDS. The immunodominant EFRHD sequence forms salt bridges, hydrogen bonds, and hydrophobic contacts, including interactions with a striking WWDDD motif of the antigen binding fragments. We also show that a similar sequence (AKFRHD) derived from the human protein GRIP1 is able to cross-react with both PFA1 and PFA2 and, when cocrystallized with PFA1, binds in an identical conformation to A{beta}(1–8). Because such cross-reactivity has implications for potential side effects of immunotherapy, our structures provide a template for designing derivative mAbs that target A{beta} with improved specificity and higher affinity

A Case Based Reasoning View of School Dropout Screening

The cause for student dropout is often termed as the antecedent of failure, since it stands for a key event, which leads to dropout. Indeed, school dropout is well thought out as one of the major worries of our times. It is a multi-layered and complex phenomenon, with many triggers, namely academic striving and failure, poor attendance, retention, disengagement from school or even socio-economic motives. School dropout represents economic and social losses to the individual, family and community. However, it may be prevented if the educational actors hold pro-active strategies (e.g., taking into account similar past experiences). Indeed, this work will start with the development of a decision support system to assess school dropout, centered on a formal framework based on Logic Programming for Knowledge Representation, complemented with a Case-Based Reasoning approach to problem solving, which caters for the handling of incomplete, unknown, or even contradictory information, i.e., it improves the analysis enactment of the retrieving cases process

The PCL Theorem. Transactions cannot be Parallel, Consistent and Live.

We show that it is impossible to design a transactional memory system which ensures parallelism, i.e. transactions do not need to synchronize unless they access the same application objects, while ensuring very little consistency, i.e. a consistency condition, called weak adaptive consistency, introduced here and which is weaker than snapshot isolation, processor consistency, and any other consistency condition stronger than them (such as opacity, serializability, causal serializability, etc.), and very little liveness, i.e. that transactions eventually commit if they run solo

Asymmetric Image-Template Registration

Authors Manuscript received: 2010 May 4. 12th International Conference, London, UK, September 20-24, 2009, Proceedings, Part IA natural requirement in pairwise image registration is that the resulting deformation is independent of the order of the images. This constraint is typically achieved via a symmetric cost function and has been shown to reduce the effects of local optima. Consequently, symmetric registration has been successfully applied to pairwise image registration as well as the spatial alignment of individual images with a template. However, recent work has shown that the relationship between an image and a template is fundamentally asymmetric. In this paper, we develop a method that reconciles the practical advantages of symmetric registration with the asymmetric nature of image-template registration by adding a simple correction factor to the symmetric cost function. We instantiate our model within a log-domain diffeomorphic registration framework. Our experiments show exploiting the asymmetry in image-template registration improves alignment in the image coordinates.NAMIC (NIH NIBIB NAMIC U54-EB005149)NAC (NIH NCRR NAC P41- RR13218)mBIRN (NIH NCRR mBIRN U24-RR021382)NIH NINDS (R01-NS051826 Grant)National Science Foundation (U.S.) (CAREER Grant 0642971)NIBIB (R01 EB001550)NIBIB (R01EB006758)NCRR (R01 RR16594-01A1)NCRR (P41-RR14075)NINDS (R01 NS052585-01)Singapore. Agency for Science, Technology and Researc

Temporal Registration in In-Utero Volumetric MRI Time Series

We present a robust method to correct for motion and deformations in in-utero volumetric MRI time series. Spatio-temporal analysis of dynamic MRI requires robust alignment across time in the presence of substantial and unpredictable motion. We make a Markov assumption on the nature of deformations to take advantage of the temporal structure in the image data. Forward message passing in the corresponding hidden Markov model (HMM) yields an estimation algorithm that only has to account for relatively small motion between consecutive frames. We demonstrate the utility of the temporal model by showing that its use improves the accuracy of the segmentation propagation through temporal registration. Our results suggest that the proposed model captures accurately the temporal dynamics of deformations in in-utero MRI time series.National Institutes of Health (U.S.) (NIH NIBIB NAC P41EB015902)National Institutes of Health (U.S.) (NIH NICHD U01HD087211)National Institutes of Health (U.S.) (NIH NIBIB R01EB017337)Wistron CorporationMerrill Lynch Wealth Management (Fellowship

Hsp70 in mitochondrial biogenesis

The family of hsp70 (70 kilodalton heat shock protein) molecular chaperones plays an essential and diverse role in cellular physiology, Hsp70 proteins appear to elicit their effects by interacting with polypeptides that present domains which exhibit non-native conformations at distinct stages during their life in the cell. In this paper we review work pertaining to the functions of hsp70 proteins in chaperoning mitochondrial protein biogenesis. Hsp70 proteins function in protein synthesis, protein translocation across mitochondrial membranes, protein folding and finally the delivery of misfolded proteins to proteolytic enzymes in the mitochondrial matrix

Temporal Registration in In-Utero Volumetric MRI Time Series

We present a robust method to correct for motion and deformations in in-utero volumetric MRI time series. Spatio-temporal analysis of dynamic MRI requires robust alignment across time in the presence of substantial and unpredictable motion. We make a Markov assumption on the nature of deformations to take advantage of the temporal structure in the image data. Forward message passing in the corresponding hidden Markov model (HMM) yields an estimation algorithm that only has to account for relatively small motion between consecutive frames. We demonstrate the utility of the temporal model by showing that its use improves the accuracy of the segmentation propagation through temporal registration. Our results suggest that the proposed model captures accurately the temporal dynamics of deformations in in-utero MRI time series.National Institutes of Health (U.S.) (NIH NIBIB NAC P41EB015902)National Institutes of Health (U.S.) (NIH NICHD U01HD087211)National Institutes of Health (U.S.) (NIH NIBIB R01EB017337)Wistron CorporationMerrill Lynch Wealth Management (Fellowship